{
  "abstract": "Semiempirical quantum chemistry (SQC) represents an invaluable asset in the toolbox of molecular and materials modeling. Thanks to their explicit quantum-mechanical treatment of electrons, they allow to capture an array of local and non-local interaction mechanisms and offers access to electronic structure properties, which can be of crucial importance in order to better understand and design molecular systems and materials. In a previous publication, we presented our recent advances within the PySEQM package, enabling rapid gradient evaluations for SQC parameters and thus developing an efficient and reliable back-end for SQC parametrizations. In this work, we detail our implementation of this parametrization procedure, SQCFit, and its features. Offering a high degree of automation, SQCFit implements a suite of tailor-made tools for the optimization of SQC parameters. Specifically, we present local versus global parametrization schemes, the use of train-validation splits and early stopping to avoid overfitting, and advanced scoring/loss functions including appropriately standardized quantitative error measures as well as more qualitative measures such as the performance in ranking candidate systems according to a given molecular property. Based on a set of test applications, we highlight how SQCFit allows to obtain performant parametrizations tailor-made for the modern use cases of SQC.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry and The PULSE Institute",
    "Stanford University",
    "SLAC National Accelerator Laboratory"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Martin Stöhr",
      "orcid": "0000-0002-4068-4692"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Todd J. Martínez",
      "orcid": "0000-0002-4798-8947"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0rgjxbpsrj7s1.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010354/v1",
  "fulltext_url": null,
  "id": "0rgjxbpsrj7s1",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010354/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7221606900",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010354/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0rgjxbpsrj7s1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010354",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010354/v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "Semiempirical Quantum Chemistry in the Age of ab initio Data and Differentiable Programming: II. Parameter Optimization",
  "title_text": "Semiempirical Quantum Chemistry in the Age of ab initio Data and Differentiable Programming: II. Parameter Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/0rgjxbpsrj7s1",
  "venue": "ChemRxiv",
  "versions": []
}